Signaled two-way avoidance learning using electrical stimulation of the inferior colliculus as negative reinforcement: effects of visual and auditory cues as warning stimuli

The inferior colliculus is a primary relay for the processing of auditory information in the brainstem. The inferior colliculus is also part of the so-called brain aversion system as animals learn to switch off the electrical stimulation of this structure. The purpose of the present study was to determine whether associative learning occurs between aversion induced by electrical stimulation of the inferior colliculus and visual and auditory warning stimuli. Rats implanted with electrodes into the central nucleus of the inferior colliculus were placed inside an open-field and thresholds for the escape response to electrical stimulation of the inferior colliculus were determined. The rats were then placed inside a shuttle-box and submitted to a two-way avoidance paradigm. Electrical stimulation of the inferior colliculus at the escape threshold (98.12 ± 6.15 (A, peak-to-peak) was used as negative reinforcement and light or tone as the warning stimulus. Each session consisted of 50 trials and was divided into two segments of 25 trials in order to determine the learning rate of the animals during the sessions. The rats learned to avoid the inferior colliculus stimulation when light was used as the warning stimulus (13.25 ± 0.60 s and 8.63 ± 0.93 s for latencies and 12.5 ± 2.04 and 19.62 ± 1.65 for frequencies in the first and second halves of the sessions, respectively, P<0.01 in both cases). No significant changes in latencies (14.75 ± 1.63 and 12.75 ± 1.44 s) or frequencies of responses (8.75 ± 1.20 and 11.25 ± 1.13) were seen when tone was used as the warning stimulus (P>0.05 in both cases). Taken together, the present results suggest that rats learn to avoid the inferior colliculus stimulation when light is used as the warning stimulus. However, this learning process does not occur when the neutral stimulus used is an acoustic one. Electrical stimulation of the inferior colliculus may disturb the signal transmission of the stimulus to be conditioned from the inferior colliculus to higher brain structures such as amygdala

Effects of aluminum sulfate on delta-aminolevulinate dehydratase from kidney, brain, and liver of adult mice

The purpose of the present study was to investigate the in vitro and in vivo effects of aluminum sulfate on delta-aminolevulinic acid dehydratase (ALA-D) activity from the brain, liver and kidney of adult mice (Swiss albine). In vitro experiments showed that the aluminum sulfate concentration needed to inhibit the enzyme activity was 1.0-5.0 mM (N = 3) in brain, 4.0-5.0 mM (N = 3) in liver and 0.0-5.0 mM (N = 3) in kidney. The in vivo experiments were performed on three groups for one month: 1) control animals (N = 8); 2) animals treated with 1 g% (34 mM) sodium citrate (N = 8) and 3) animals treated with 1 g% (34 mM) sodium citrate plus 3.3 g% (49.5 mM) aluminum sulfate (N = 8). Exposure to aluminum sulfate in drinking water inhibited ALA-D activity in kidney (23.3 ± 3.7%, mean ± SEM, P<0.05 compared to control), but enhanced it in liver (31.2 ± 15.0%, mean ± SEM, P<0.05). The concentrations of aluminum in the brain, liver and kidney of adult mice were determined by graphite furnace atomic absorption spectrometry. The aluminum concentrations increased significantly in the liver (527 ± 3.9%, mean ± SEM, P<0.05) and kidney (283 ± 1.7%, mean ± SEM, P<0.05) but did not change in the brain of aluminum-exposed mice. One of the most important and striking observations was the increase in hepatic aluminum concentration in the mice treated only with 1 g% sodium citrate (34 mM) (217 ± 1.5%, mean ± SEM, P<0.05 compared to control). These results show that aluminum interferes with delta-aminolevulinate dehydratase activity in vitro and in vivo. The accumulation of this element was in the order: liver > kidney > brain. Furthermore, aluminum had only inhibitory properties in vitro, while in vivo it inhibited or stimulated the enzyme depending on the organ studied.

Adsorption of arsenic from ammonia containing waste water by ferrous hydroxide waste

Arsenic is a toxic substance. The amount of arsenic in waste water is a raising problem because of increasing mining industry. Arsenic is connected to cancers in areas where arsenic concentration in drinking water is higher than recommendations. The main object in this master’s thesis was to research how ferrous hydroxide waste material is adsorbed arsenic from ammonia containing waste water. In this master’s thesis there is two parts: theoretical and experimental part. In theoretical part harmful effects of arsenic, theory of adsorption, isotherms modeling of adsorption and analysis methods of arsenic are described. In experimental part adsorption capacity of ferrous hydroxide waste material and adsorption time with different concentrations of arsenic were studied. Waste material was modified with two modification methods. Based on experimental results the adsorption capacity of waste material was high. The problem with waste material was that at same time with arsenic adsorption sulfur was dissolving in solution. Waste material was purified from sulfur but purification methods were not efficient enough. Purification methods require more research.

Coronary artery plaque burden and calcium scores in healthy men adhering to long-term wine drinking or alcohol abstinence

Observational studies suggest there are clinical benefits to moderate red wine (RW) consumption. However, the effects on coronary vasculature and overall lifestyle are unclear. We investigated whether a lifestyle of regular long-term RW consumption is associated with changes in coronary plaque burden, calcium score, carotid intima/media thickness, endothelial function, and metabolic variables, compared with alcohol abstinence. Healthy volunteers were evaluated by coronary computed tomography angiography (CTA) as well as carotid and brachial artery ultrasound. Nutritional status, psychological status, and metabolic variables were assessed. The study included 101 drinkers [aged 58.9±7.3 years (means±SD)], from wine brotherhoods, and 104 abstainers, from Anglican, Evangelical and Catholic churches both in the city of São Paulo, Brazil. No significant differences in demographics were noted. Lesion prevalence per patient assessed by coronary CTA and classified as absent (0), 1-25, 26-49, and ≥50% stenosis was similar between groups. When analyzed by individual arteries, i.e., left anterior descending, circumflex, and right coronary, prevalence was also not different. On the other hand, calcium scores were higher among drinkers than abstainers (144.4±362.2 vs 122.0±370.3; P<0.01). However, drinkers reported less history of diabetes and exercised more. RW drinkers consumed 2127.9±387.7 kcal/day while abstainers consumed 1836.0±305.0 (P<0.0001). HDL cholesterol was significantly higher among drinkers compared to abstainers (46.9±10.9 vs 39.5±9.0 mg/dL; P<0.001), while fasting plasma glucose was lower (97.6±18.2 vs 118.4±29.6 mg/dL; P<0.02). Liver enzymes were normal in both groups. In conclusion, long-term wine drinkers displayed a similar plaque burden but greater calcium score than abstainers, despite a more atherogenic diet, and the mechanisms for the increased calcium scores in the former remain speculative.

Vulnerability to adverse consequences of drinking and problem drinker status as predicted by risky drinking behaviours, drug use, sex differences and affect : a test of multiple models /

Although alcohol problems and alcohol consumption are related, consumption does not fully account for differences in vulnerability to alcohol problems. Therefore, other factors should account for these differences. Based on previous research, it was hypothesized that risky drinking behaviours, illicit and prescription drug use, affect and sex differences would account for differences in vulnerability to alcohol problems while statistically controlling for overall alcohol consumption. Four models were developed that were intended to test the predictive ability of these factors, three of which tested the predictor sets separately and a fourth which tested them in a combined model. In addition, two distinct criterion variables were regressed on the predictors. One was a measure of the frequency that participants experienced negative consequences that they attributed to their drinking and the other was a measure of the extent to which participants perceived themselves to be problem drinkers. Each of the models was tested on four samples from different populations, including fIrst year university students, university students in their graduating year, a clinical sample of people in treatment for addiction, and a community sample of young adults randomly selected from the general population. Overall, support was found for each of the models and each of the predictors in accounting for differences in vulnerability to alcohol problems. In particular, the frequency with which people become intoxicated, frequency of illicit drug use and high levels of negative affect were strong and consistent predictors of vulnerability to alcohol problems across samples and criterion variables. With the exception of the clinical sample, the combined models predicted vulnerability to negative consequences better than vulnerability to problem drinker status. Among the clinical and community samples the combined model predicted problem drinker status better than in the student samples.

Motivational model of problem drinking for men and women /

This thesis tested a path model of the relationships of reasons for drinking and reasons for limiting drinking with consumption of alcohol and drinking problems. It was hypothesized that reasons for drinking would be composed of positively and negatively reinforcing reasons, and that reasons for limiting drinking would be composed of personal and social reasons. Problem drinking was operationalized as consisting of two factors, consumption and drinking problems, with a positive relationship between the two. It was predicted that positively and negatively reinforcing reasons for drinking would be associated with heavier consumption and, in turn, more drinking problems, through level of consumption. Negatively reinforcing reasons were also predicted to be associated with drinking problems directly, independent of level of consumption. It was hypothesized that reasons for limiting drinking would be associated with lower levels of consumption and would be related to fewer drinking problems, through level of consumption. Finally, among women, reasons for limiting drinking were expected to be associated with drinking problems directly, independent of level of consumption. The sample, was taken from the second phase of the Niagara Young Aduh Health Study, a community sample of young adult men and women. Measurement models of reasons for drinking, reasons for limiting drinking, and problem drinking were tested using Confirmatory Factor Analysis. After adequate fit of each measurement model was obtained, the complete structural model, with all hypothesized paths, was tested for goodness of fit. Cross-group equality constraints were imposed on all models to test for gender differences. The results provided evidence supporting the hypothesized structure of reasons for drinking and problem drinking. A single factor model of reasons for limiting drinking was used in the analyses because a two-factor model was inadequate. Support was obtained for the structural model. For example, the resuhs revealed independent influences of Positively Reinforcing Reasons for Drinking, Negatively Reinforcing Reasons for Drinking, and Reasons for Limiting Drinking on consumption. In addition. Negatively Reinforcing Reasons helped to account for Drinking Problems independent of the amount of alcohol consumed. Although an additional path from Reasons for Limiting Drinking to Drinking Problems was hypothesized for women, it was of marginal significance and did not improve the model's fit. As a result, no sex differences in the model were found. This may be a result of the convergence of drinking patterns for men and women. Furthermore, it is suggested that gender differences may only be found in clinical samples of problem drinkers, where the relative level of consumption for women and men is similar.

Attachment and alcohol : testing a motivational model of problem drinking /

The purpose of this study was to replicate and extend a motivational model of problem drinking (Cooper, Frone, Russel, & Mudar, 1995; Read, Wood, Kahler, Maddock & Tibor, 2003), testing the notion that attachment is a common antecedent for both the affective and social paths to problem drinking. The model was tested with data from three samples, first-year university students (N=679), students about to graduate from university (N=206), and first-time clients at an addiction treatment facility (N=21 1). Participants completed a battery of questionnaires assessing alcohol use, alcohol-related consequences, drinking motives, peer models of alcohol use, positive and negative affect, attachment anxiety and attachment avoidance. Results underscored the importance of the affective path to problem drinking, while putting the social path to problem drinking into question. While drinking to cope was most prominent among the clinical sample, coping motives served as a risk factor for problem drinking for both individuals identified as problem drinkers and university students. Moreover, drinking for enhancement purposes appeared to be the strongest overall predictor of alcohol use. Results of the present study also supported the notion that attachment anxiety and avoidance are antecedents for the affective path to problem drinking, such that those with higher levels of attachment anxiety and avoidance were more vulnerable to experiencing adverse consequences related to their drinking, explained in terms of diminished affect regulation. Evidence that nonsecure attachment is a potent predictor of problem drinking was also demonstrated by the finding that attachment anxiety was directly related to alcohol-related consequences over and above its indirect relationship through affect regulation. However, results failed to show that attachment anxiety or attachment avoidance increased the risk of problem drinking via social influence.

Antidiabetic activity of Vaccinium vitis-idaea, a medicinal plant from the traditional pharmacopeia of the James Bay Cree

L’incidence du diabète chez les premières nations du Canada est plus de trois fois celle du reste du pays, dû, en partie, aux traitements culturellement inappropriés. Notre projet vise à traiter le diabète chez ces populations à partir de leur pharmacopée de médicine traditionnelle afin d’améliorer l’acceptation des traitements. En utilisant une approche ethnobotanique, notre équipe a identifié 17 plantes médicinales utilisées pour traiter des symptômes du diabète par les Cris d'Eeyou Istchee (Baie James, Québec). Parmi eux, l'extrait éthanolique de baies de Vaccinium vitis-idaea a montré un effet stimulateur sur le transport du glucose dans les cellules musculaires squelettiques et les adipocytes en culture. Le but de cette thèse était d’élucider les mécanismes par lesquels cet extrait exerce ses effets anti-hyperglycémiants, d’identifier ses principes actifs et de confirmer in vivo, son efficacité. Les résultats démontrent que V.vitis a augmenté le transport du glucose dans les cellules musculaires en cultures, C2C12 et L6 et a stimulé la translocation des transporteurs GLUT4 dans les cellules L6. L'extrait a également inhibé la respiration dans les mitochondries isolées du foie du rat. Cet effet est semblable à celui de la metformine et en lien avec la production du stress métabolique et l'activation de l'AMPK. De plus, la voie de signalisation de l’insuline ne semble pas être impliquée dans le mécanisme d’action de V. vitis. Le fractionnement guidé par la stimulation du transport du glucose a mené à l'isolation des principes actifs; la quercétine, la quercétine-3-O-galactoside, et la quercétine-3-O-glucoside. Comparable à l'extrait brut, ses composés ont stimulé la voie AMPK. Cependant, la quércetine était la seule à inhiber la respiration mitochondriale. Pour valider l'effet de V.vitis in vivo, l'extrait (1% dans l'eau de boisson) a été administré aux souris KKAy pendant 10 jours. La glycémie et le poids corporel ont été significativement réduits par V.vitis. Ces effets ont été associés à une diminution de la prise alimentaire, ce qui suggère que V.vitis diminue l'appétit. L'étude pair-fed a confirmé que les effets de V.vitis sont, majoritairement, dû à la réduction de l’appétit. De plus, V.vitis a augmenté la teneur en GLUT4 dans le muscle squelettique, a stimulé la iv phosphorylation de l'ACC et a augmenté les niveaux de PPAR-α dans le foie des souris KKAy. Ces effets se voient être additifs à l’effet anorexigène de V. vitis. Au cours du fractionnement bioguidé de l’extrait, l’ester méthylique de l'acide caféique (CAME), un produit formé lors de la procédure du fractionnement, a démontré un effet stimulateur puissant sur le transport du glucose dans les celules C2C12 et donc un potentiel anti-diabétique. Pour identifier d'autres acides caféique active (AC) et pour élucider leurs relations structure-activité et structure-toxicité, vingt dérivés AC ont été testés. Outre CAME, quatre composés ont stimulé le transport du glucose et ont activé l'AMPK suite au stress métabolique résultant d'un découplage de la phosphorylation oxydative mitochondriale. L’activité nécessite une fonction d’AC intacte dépourvu de groupements fortement ionisés et ceci était bien corrélée avec la lipophilicite et la toxicité. Les résultats de cette thèse soutiennent le potentiel thérapeutique de V. vitis, ses composés actifs ainsi que de la famille de l’AC et pour la prévention et le traitement du diabète.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Water Quality Variation, its Influence on Drinking Water Treatment and Remedial Measures - A Case Study of Peechi reservoir, Thrissur, Kerala

The Kerala Water Authority requested the School of Environmental Studies to carry out investigations on the mechanism of sporadic mobilization of iron and odour in the raw water drawn to the drinking water treatment plant. The currently used treatment process failed to remove iron completely. This led to problems in the filter and complaints of taste and colour due to iron in the finished water. The sporadic nature of the problem itself made the trouble shooting difficult. The problem was looked in from three points of view. 1. Influence of environmental (climatic) conditions on the dynamics of the relevant basin of the reservoir. 2. Influence of the physical dynamics on the physico — chemical quality of water. 3. Identification of cost-effective treatment processes to suit the existing plant. Since the problem emerged only during the post- monsoon to pre-monsoon months, a related problem was investigated, namely, influence of anions on the oxidation of Fe(II) in natural waters by air. This is presented in Part II of the dissertation.

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.

Performance of hybrid rebars as longitudinal reinforcement in normal strength concrete

A/though steel is most commonly used as a reinforcing material in concrete due to its competitive cost and favorable mechanical properties, the problem of corrosion of steel rebars leads to a reduction in life span of the structure and adds to maintenance costs. Many techniques have been developed in recent past to reduce corrosion (galvanizing, epoxy coating, etc.) but none of the solutions seem to be viable as an adequate solution to the corrosion problem. Apart from the use of fiber reinforced polymer (FRP) rebars, hybrid rebars consisting of both FRP and steel are also being tried to overcome the problem of steel corrosion. This paper evaluates the performance of hybrid rebars as longitudinal reinforcement in normal strength concrete beams. Hybrid rebars used in this study essentially consist of glass fiber reinforced polymer (GFRP) strands of 2 mm diameter wound helically on a mild steel core of 6 mm diameter. GFRP stirrups have been used as shear reinforcement. An attempt has been made to evaluate the flexural and shear performance of beams having hybrid rebars in normal strength concrete with and without polypropylene fibers added to the concrete matrix

Rice husk ash – A valuable reinforcement for high density polyethylene

This paper presents the results of a study on the use of rice husk ash (RHA) for property modification of high density polyethylene (HDPE). Rice husk is a waste product of the rice processing industry. It is used widely as a fuel which results in large quantities of RHA. Here, the characterization of RHA has been done with the help of X-ray diffraction (XRD), Inductively Coupled Plasma Atomic Emission Spectroscopy (ICPAES), light scattering based particle size analysis, Fourier transform infrared spectroscopy (FTIR) and Scanning Electron Microscope (SEM). Most reports suggest that RHA when blended directly with polymers without polar groups does not improve the properties of the polymer substantially. In this study RHA is blended with HDPE in the presence of a compatibilizer. The compatibilized HDPE-RHA blend has a tensile strength about 18% higher than that of virgin HDPE. The elongation-at-break is also higher for the compatibilized blend. TGA studies reveal that uncompatibilized as well as compatibilized HDPERHA composites have excellent thermal stability. The results prove that RHA is a valuable reinforcing material for HDPE and the environmental pollution arising from RHA can be eliminated in a profitable way by this technique.

A Reinforcement-Learning Approach to Power Management

We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our framework can be applied to the power management problem in both infrastructure and ad~hoc wireless networks. From this thesis we conclude that mid-level power management policies can outperform low-level policies and are more convenient to implement than high-level policies. We also conclude that power management policies need to adapt to the user and network, and that a mid-level power management framework based on reinforcement learning fulfills these requirements.

Reinforcement Learning by Policy Search

One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. This means learning a policy---a mapping of observations into actions---based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multi-agent systems. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience re-use. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.